# CrowdFix: An Eyetracking Dataset of Real Life Crowd Videos

arXiv: https://arxiv.org/abs/1910.02618 · HTML: https://mlsanity.com/paper/1910.02618/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2019.

## Built on by (most cited first, 1 of 1)

- [DensFiLM: Density-Conditioned Video Saliency for Crowd Scenes](https://mlsanity.com/paper/2607.25465/) (2026)

## Abstract (arXiv)

Understanding human visual attention and saliency is an integral part of vision research. In this context, there is an ever-present need for fresh and diverse benchmark datasets, particularly for insight into special use cases like crowded scenes. We contribute to this end by: (1) reviewing the dynamics behind saliency and crowds. (2) using eye tracking to create a dynamic human eye fixation dataset over a new set of crowd videos gathered from the Internet. The videos are annotated into three distinct density levels. (3) Finally, we evaluate state-of-the-art saliency models on our dataset to identify possible improvements for the design and creation of a more robust saliency model.